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Acquire practical skills in Big Data Analytics and explore data science with Apache Mahout About This BookLearn to use Apache Mahout for Big Data AnalyticsUnderstand machine learning concepts and algorithms and their implementation in Mahout.A comprehensive guide with numerous code examples and end-to-end case studies on Customer Analytics and Text Analytics.Who This Book Is ForIf you are a Java developer and want to use Mahout and machine learning to solve Big Data Analytics use cases then this book is for you. Familiarity with shell scripts is assumed but no prior experience is required.What You Will Learn Configure Mahout on Linux systems and set up the development environment Become familiar with the Mahout command line utilities and Java APIs Understand the core concepts of machine learning and the classes that implement them Integrate Apache Mahout with newer platforms such as Apache Spark Solve classification, clustering, and recommendation problems with Mahout Explore frequent pattern mining and topic modeling, the two main application areas of machine learning Understand feature extraction, reduction, and the curse of dimensionality In DetailIn the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable[...]

Key FeaturesApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset from the churn dataset, applying different classification methodsBook DescriptionThe R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.What you will learnCreate and inspect the transaction dataset, performing association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsPredict possible churn users with the classification approachImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on Big DataAbout the AuthorYu-Wei[...]

In DetailBig data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing.Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.ApproachBig Data Analytics with R and Hadoop is[...]

Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.Big[...]

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance.Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution.When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time.This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka[...]

Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You’ll explore the theory behind time series databases and learn practical methods for implementing them. Authors Ted Dunning and Ellen Friedman provide a detailed examination of open source tools such as OpenTSDB and new modifications that greatly speed up data ingestion.You’ll learn:A variety of time series use casesThe advantages of NoSQL databases for large-scale time series dataNoSQL table design for high-performance time series databasesThe benefits and limitations of OpenTSDBHow to access data in OpenTSDB using R, Go, and RubyHow time series databases contribute to practical machine learning projectsHow to handle the added complexity of geo-temporal dataFor advice on analyzing time series data, check out Practical Machine Learning: A New Look at Anomaly Detection, also from Ted Dunning and Ellen Friedman[...]

Beginning Oracle Database 12c Administration is your entry point into a successful and satisfying career as an Oracle Database Administrator.The chapters of this book are logically organized into four parts closely tracking the way your database administration career will naturally evolve. Part 1 "Database Concepts" gives necessary background in relational database theory and Oracle Database concepts, Part 2 "Database Implementation" teaches how to implement an Oracle database correctly, Part 3 "Database Support" exposes you to the daily routine of a database administrator, and Part 4 "Database Tuning" introduces the fine art of performance tuning.Beginning Oracle Database 12c Administration provides information that you won't find in other books on Oracle Database. You'll discover not only technical information, but also guidance on work practices that are as vital to your success as are your technical skills. The author's favorite chapter is "The Big Picture and the Ten Deliverables." (It is the editor’s favorite chapter too!) If you take the lessons in that chapter to heart, you can quickly become a much better Oracle database administrator than you ever thought possible.You will grasp the key aspects of theory behind relational database management systems and learn how to:• Install and configure an Oracle database[...]

SummaryHadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the BookIt's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date[...]